{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "P2mhThKkdK0R",
        "outputId": "4f658d22-f17b-4733-e0f7-d8035013d688"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f539c5e4d30>"
            ]
          },
          "metadata": {},
          "execution_count": 1
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pylab as plt\n",
        "\n",
        "1,2,3,5,7\n",
        "10,25,50,75,100,125\n",
        "dat = np.array([[4e-2, 1e-2, 0.7, 0.8e-2 , 0.1, 0.1,0.1],[0.1,0.98,0.97,0.25e-2, 0.1,0.1 , 0.1] ,[0.8,0.5,0.7,1e-2, 3e-3,1e-4,3e-4], [0.7,0.6,0.98,0.4e-3,5e-6, 3e-6,0.31e-6],[5,0.3, 0.1,0.1 , 0.3e-6,0.27e-6,5e-8]])\n",
        "dat = np.array([[4e-2, 1e-2, 0.7, 0.8e-2 , 0.1, 0.1,0.1],[0.1,0.98,0.97,0.25e-2, 0.1,0.1 , 0.1] ,[0.8,0.5,0.7,1e-2, 3e-3,1e-4,3e-4], [0.7,0.6,0.98,0.4e-3,5e-6, 3e-6,0.31e-6],[5,0.3, 0.1,0.1 , 0.3e-6,0.27e-6,5e-8]])\n",
        "\n",
        "dat = np.array([[1e0, 1e0, 1e0, 0.9e0 , 0.9e0, 0.9e0],[4e-2, 0.95e-2, 1e-2, 0.6e-2,4e-3, 2e-3] ,[2e-2, 0.7e-2,0.8e-2,1e-3,2e-3,1e-4], [1e-2,0.75e-2,0.8e-2,0.3e-3,0.5e-3,1e-4],[0.4e-2, 0.8e-2,0.3e-2,0.3e-4,1e-3,0.2e-3]])\n",
        "\n",
        "\n",
        "import numpy as np\n",
        "import seaborn as sns; sns.set_theme(style='white')\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.colors import LogNorm, Normalize\n",
        "from matplotlib.ticker import MaxNLocator\n",
        "\n",
        "\n",
        "\n",
        "f3, ax5 = plt.subplots(1,1)\n",
        "sns.heatmap(dat, square=True, norm=LogNorm())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dat"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Y4xZ7DIdLTj",
        "outputId": "4d8716ad-7312-46cd-f14a-e5cd98cf9eab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3, 2, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pylab as plt\n",
        "\n",
        "import pandas as pd\n",
        "df1 = pd.read_excel('/content/exp_6_levin1.xlsx')\n",
        "df2 = pd.read_excel('/content/exp_6_flops_levin1.xlsx')"
      ],
      "metadata": {
        "id": "sBQH43IZXkXv"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "row1 = []\n",
        "row2 = []\n",
        "\n",
        "for i in range(df1.shape[0]):\n",
        "  row_list = df1.loc[i, :].values.flatten().tolist()\n",
        "  row1.append(row_list)\n",
        "  \n",
        "for i in range(df2.shape[0]):\n",
        "  row_list = df2.loc[i, :].values.flatten().tolist()\n",
        "  row2.append(row_list) "
      ],
      "metadata": {
        "id": "TyxEoYQDX0_0"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import seaborn as sns; sns.set_theme(style='white')\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.colors import LogNorm, Normalize\n",
        "from matplotlib.ticker import MaxNLocator"
      ],
      "metadata": {
        "id": "5yf7DTROZr1r"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dat1 = np.array(row1)\n",
        "dat2 = np.array(row2)"
      ],
      "metadata": {
        "id": "UUV5SDdPdX90"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "f3, ax5 = plt.subplots(1,1)\n",
        "sns.heatmap(dat1, square=True, norm=LogNorm())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        },
        "id": "1ui7Wce_ZAOi",
        "outputId": "ac4bb6e0-34e5-459a-c733-9590df18b532"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd98cf301f0>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f3, ax5 = plt.subplots(1,1)\n",
        "sns.heatmap(dat2, square=True, norm=LogNorm())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "OxW6vp4UZwCZ",
        "outputId": "46787ab8-a56f-47bb-8a94-97561e25671d"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd98cf04190>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2NzWfitbZ1O9"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}